Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture

A Distributed Intelligent Sensing Approach for Environmental Monitoring Applications

Scientific reports from around the world present us with the undeniable fact that the global ecosystem is undergoing severe change. As this shift accelerates, it is ever more critical that we are able to quantify the local effects of such changes, and further, their implications, from our daily life to the biological processes that put food on our tables. In this thesis, we study the application of sensor network technology to the observation and estimation of highly local phenomena---specifically at a scale between ten to several hundred square meters. Embedding knowledge about the observed process directly into the sensor nodes' behavior via dedicated resource management or control algorithms allows us to deploy dense networks with low power requirements. Ecological systems are notoriously complex. In our work we must thus be highly experimental; it is our highest goal that we construct an approach to environmental monitoring that is not only realistic, but practical for real-world use. Our approach is centered on a commercially available sensor network product, aided by an off-the-shelf quadrotor with minimal customization. We validate our approach through a series of experiments performed from simulation all the way to reality, in deployments lasting days to several months. We motivate the need for local data via two case studies examining physical phenomena. First, employing novel modalities, we study the eclosion of a common agricultural pest. We present our efforts to acquire data that is more local than commonly employed methods, culminating in a six month deployment in a Swiss apple orchard. Next, we apply a environmental fluid dynamics model to enable the estimation of sensible heat flux using an inexpensive sensor. We integrate the sensor with a wireless sensor network and validate its capabilities in a short-term deployment. Acquiring meaningful data on a local scale requires that we advance the state of the art in multiple aspects. Static sensor networks present a classical tension between resolution, autonomy, and accuracy. We explore the performance of algorithms aimed at providing all three, showing explicitly what is required to implement these approaches for real-world applications in an autonomous deployment under uncontrolled conditions. Eventually, spatial resolution is limited by network density. Such limits may be overcome by the use of mobile sensors. We explore the use of an off-the-shelf quadrotor, equipped with environmental sensors, as an additional element in system of heterogeneous sensing nodes. Through a series of indoor and outdoor experiments, we quantify the contribution of a such a mobile sensor, and various strategies for planning its path

Modelling and analysis of plant image data for crop growth monitoring in horticulture

Plants can be characterised by a range of attributes, and measuring these attributes accurately and reliably is a major challenge for the horticulture industry. The measurement of those plant characteristics that are most relevant to a grower has previously been tackled almost exclusively by a combination of manual measurement and visual inspection. The purpose of this work is to propose an automated image analysis approach in order to provide an objective measure of plant attributes to remove subjective factors from assessment and to reduce labour requirements in the glasshouse. This thesis describes a stereopsis approach for estimating plant height, since height information cannot be easily determined from a single image. The stereopsis algorithm proposed in this thesis is efficient in terms of the running time, and is more accurate when compared with other algorithms. The estimated geometry, together with colour information from the image, are then used to build a statistical plant surface model, which represents all the information from the visible spectrum. A self-organising map approach can be adopted to model plant surface attributes, but the model can be improved by using a probabilistic model such as a mixture model formulated in a Bayesian framework. Details of both methods are discussed in this thesis. A Kalman filter is developed to track the plant model over time, extending the model to the time dimension, which enables smoothing of the noisy measurements to produce a development trend for a crop. The outcome of this work could lead to a number of potentially important applications in horticulture.EThOS - Electronic Theses Online ServiceHorticultural Development Council (Great Britain) (HDC) (CP 37)GBUnited Kingdo